Characterisation of source rocks is important for evaluation of both conventional and unconventional reservoirs. Organic matter is deposited and preserved at the bottom of lakes, seas and deltas. As more material is deposited, the organic matter is buried and the heat and pressure of burial transforms the organic matter into geopolymers such as kerogen and bitumen. When the rocks containing organic matter are buried deep enough, the rocks undergo catagenesis, where temperature begins to convert the kerogen into bitumen and ultimately into hydrocarbons such as oil and gas. The rocks that produce hydrocarbons are referred to as source rocks. Porosity in organic matter is often the predominant type of total porosity development in source rocks. Due to the hydrophobic nature of organic matter, organic porosity, which also can be referred to as porosity associated with organic matter (“PAOM”), is in most situations fully occupied by hydrocarbons, whereas water resides in intraparticle and intergranular pores of inorganic material.
Unconventional resources have emerged as a major source of hydrocarbon production in the United States and other areas. As more information has emerged about these organic rich, fine grained rock formations, often referred to as shale, it has become apparent that the one of the important characteristics is the quantity of porosity that has evolved from what was originally kerogen, bitumen, or other solid organic matter. Porosity in organic matter is a function of the degree of thermal maturity and the resultant decomposition of organic material during the hydrocarbon generation process. From evaluation of high magnification ion-milled SEM images, researchers have observed that shale pore space can be broadly divided into three types, inter-granular, intra-granular and organic matter associated. E.g., Loucks, R. G., et al., 2009, Morphology, Genesis, and Distribution of Nanometer-Scale Pores in Siliceous Mudstones of the Mississippian Barnett Shale, Journal of Sedimentary Research, v. 79, p. 848-861, doi: 10.2110/jsr.2009.092; Loucks, R. G., et al., 2010, Preliminary Classification of Matrix Pores in Mudrocks, Gulf Coast Association of Geological Societies, Transactions, v. 60, p. 435-441; Passey, Q. R., et al., 2010, From Oil-Prone Source Rock to Gas-Producing Shale Reservoir-Geologic and Petrophysical Characterization of Unconventional Shale-Gas Reservoirs, SPE, Chinese Petroleum Society and Society of Petroleum Engineers International Oil and Gas Conference and Exhibition in China, June 8-10, Beijing, China, SPE Paper 131350, 29 p., doi: 10.2118/131350-MS.
Porosity development in organic matter has been observed and organic porosity area calculations have been performed based on such ion milled SEM image analyses. The reliance on ion milled SEM image-based analyses for evaluating geological samples one at a time for PAOM can be problematic since this approach can be time-consuming and costly.
There has not been a reliable method for computing PAOM from commonly available well log data or from core computer tomographic (CT) scan data.
Well log analysis is a common and important part of evaluating hydrocarbon bearing formations for porosity, oil, gas, and water content. Numerous methods exist for computing total and effective porosity from electrical resistivity, bulk density, neutron porosity, and other log analysis measurements. However, shale wells have presented a particularly difficult problem for well log analysis because many traditional methods that work for sandstones and carbonates do not work well for organic shales. There has been no commonly available method to compute PAOM from typical well log data. If certain advanced technology well logs are obtained such as nuclear magnetic resonance or dielectric properties, then it may be theoretically possible to compute PAOM, but these logs are often either unavailable or unreliable in shale formations and are more expensive than common logs like the “triple-combo.
Recently, methods have been developed to compute porosity, clay content, organic matter content and other reservoir properties from bulk density (RHOB) and photo-electric effect (PEF) from X-ray CT scans plus spectral gamma ray data on core samples, which involves integrating a number of different forms of analysis. E.g., U.S. Patent Application Publication No. 2013/0182819 A1. There is no known method, however, to compute PAOM for any location along the length of a whole core from available data such as bulk density, PEF, and spectral gamma ray data.
An equation for directly calculating PAOM from other determined parameter values has been suggested. A suggested equation for calculating PAOM has been expressed as: organic porosity (% rock volume)=TR(fraction)*HI (mg/gTOC)*TOC (% weight)*2.5/1.2/1150, where TR is transformation ratio (the fraction of the labile kerogen that has already converted to petroleum), and HI is hydrogen index when it was immature, and TOC is original TOC, and the constant 2.5 is rock density and 1.2 is kerogen density in g/cc, and 1150 is the equivalent HI of hydrocarbons. The above equation is based on geochemical data and several assumptions. The major unknown in the equation is TR. In order to get a value for TR one has to know the amount of convertible kerogen that has already converted to hydrocarbon. Making this determination of convertible kerogen is not easy and usually requires knowledge of the TOC of the immature kerogen at the location. Data on the immature kerogen is not typically available, and TR has not been simple to quantify. In U.S. Patent Application Publication No. 2014/0052420 A1, a maturity level of a rock sample can be estimated with calculation of a conversion ratio (CR) as PAOM/(PAOM+OM), and the CR may be correlated directly to a maturity level value of the rock sample.
It is desirable to have a method that can be used to more readily determine PAOM for geological samples acquired from a prospective well or formation along the vertical extent, or horizontal extent, or both of a wellbore or formation in a reliable manner. It further is desirable to have such a method for determining PAOM which can use more readily obtainable or commonly available well log data or core CT scan data.